Some of the more popular digital image compression techniques used in communication and data storage devices include various standardized compression coding methods. For example, such compression techniques utilize coding methods developed by the Moving Pictures Expert Group (MPEG) or the Joint Photographic Experts Group (JPEG) in which discrete cosine transform (DCT) and Huffman coding are used. Furthermore, the methods may also utilize vector quantization or sub-band coding.
A block diagram of a conventional compression coding device which uses an MPEG or JPEG compression coding method is shown in FIG. 1. As illustrated in the figure, the device comprises a compression system for compressing digital data and a decompression system for decompressing digital data. In particular, the compression system comprises an input buffer 50, a transform unit 52, a quantizer 54, a Huffman coding unit 56, and a transmitter/recorder 58. In addition, the decompression system contains a receiver/reproducer 60, a Huffman decoding unit 62, an inverse quantizer 64, an inverse transform unit 66, and an output buffer 68.
In order to compress a digital input image, the image is input to the transform unit 52 via the buffer 50 and is transformed according to a DCT function to produce various transform coefficients which correspond to the image. Subsequently, the transform coefficients are output to and quantized by the quantizer 54. Furthermore, the coefficients are not quantized by subdividing them by a uniform interval, but are differentially subdivided by a spatial frequency by using a human visual system (HVS) 70. Then, the quantized coefficients are compressed by the Huffman coding unit 56 in accordance with appropriate statistical characteristics corresponding to the input image. Finally, the compressed data is transmitted to a receiver or recorded on a recording medium by the transmitter/recorder 58.
In addition, compressed data may be expanded into a restored image by the decompression system. In particular, the components of the decompression system perform functions which are similar but opposite to the functions executed by the compression system.
In the DCT compression coding method shown in FIG. 1, an input image is divided into many uniform blocks and a cosine function kernel is applied to each block to enhance the compression by preventing the generation of an overlapping image. However, even though a high compression rate may be attained, a severe blocking effect is generated.
Also, the vector quantization method utilized by the compression system is also advantageous due to its contribution to the high compression rate. However, since such method requires excessive calculations for a code-book training process and data compression, it cannot be used for real time systems.
On the other hand, the sub-band method reduces the blocking effect which occurs during high rates of data compression and is more efficient than conventional DCT methods. However, such method cannot obtain a high quality image since it employs a low compression rate.
Therefore, in order to overcome the above problems, a wavelet transform (WT) method has been introduced. Since this method encodes image signals based on time and frequency, the wavelet transform (WT) method is useful for analyzing non-stationary signals and is advantageous because it is similar to the human visual system (HVS).
Wavelet transformation (WT) is an integrated theory comprising a multi-resolution analysis of sub-band coding and a conventional method in which images are divided into a plurality of sub-images that are expressed as a pyramidal structure. In other words, each sub-image has hierarchical information ranging from a low-frequency band to a high-frequency band such that more appropriate coding can be performed.